hQSrSSKJr SSKJrJr SSKJr SSKJ r SSK J r J r J r JrJr SSKrSSKJr S S KJr S S KJrJr \ (aSSKr\\ S \4rSS jrSSjrS SjrS!Sjr "SS\\5r!"SS\5r"S"Sjr#S#Sjr$S$Sjr%S%Sjr&S&Sjr'S'Sjr(\S(S)Sjj5r)\S(S*Sjj5r)S(S+Sjjr)g),aModule containing utility functions and classes for the core Albumentations framework. This module provides a collection of helper functions and base classes used throughout the Albumentations library. It includes utilities for shape handling, parameter processing, data conversion, and serialization. The module defines abstract base classes for data processors that implement the conversion logic between different data formats used in the transformation pipeline. ) annotations)ABCabstractmethod)Sequence)Real) TYPE_CHECKINGAnyLiteralcastoverloadN) LabelManager) Serializable)PAIRNumberdepthheightwidthc[U[R5(aURSS$SSKnUR "U5(aURSS$[S[U535e![ a N#f=f)a2Extract height and width dimensions from an image. Args: img (np.ndarray | torch.Tensor): Image as either numpy array (HWC format) or torch tensor (CHW format). Returns: tuple[int, int]: Image dimensions as (height, width). Raises: RuntimeError: If the image type is not supported. NrzUnsupported image type: isinstancenpndarrayshapetorch is_tensor ImportError RuntimeErrortype)imgrs S/var/www/fran/franai/venv/lib/python3.13/site-packages/albumentations/core/utils.pyget_image_shaper%s~#rzz""yy!}  ??3  99RS> !  1$s)= >>    )A22 A?>A?c[U[R5(aURSS$SSKnUR "U5(aURSS$[S[U535e![ a N#f=f)aKExtract depth, height, and width dimensions from a volume. Args: vol (np.ndarray | torch.Tensor): Volume as either numpy array (DHWC format) or torch tensor (CDHW format). Returns: tuple[int, int, int]: Volume dimensions as (depth, height, width). Raises: RuntimeError: If the volume type is not supported. NrzUnsupported volume type: r)volrs r$get_volume_shaper+8s~#rzz""yy!}  ??3  99RS> !  249+> ??    r&cSU;a[US5upnXUS.$SU;a[US5up#X#S.$SU;a[USS5up#X#S.$[SUR55e)a Extract spatial dimensions from input data dictionary containing image or volume. Args: data (dict[str, Any]): Dictionary containing image or volume data with one of: - 'volume': 3D array of shape (D, H, W, C) [numpy] or (C, D, H, W) [torch] - 'image': 2D array of shape (H, W, C) [numpy] or (C, H, W) [torch] - 'images': Batch of arrays of shape (N, H, W, C) [numpy] or (N, C, H, W) [torch] Returns: dict[Literal["depth", "height", "width"], int]: Dictionary containing spatial dimensions volumerimage)rrimagesrz No image or volume found in data)r+r% ValueErrorkeys)datarrrs r$ get_shaper3Rs4/X?u5AA$'W 6  114'Xq(9:  11 7 EEc/nUR5HAup#[U[5(aSUS3O [U5nURUSU35 MC SR U5$)zFormat a dictionary of arguments into a string representation. Args: args_dict (dict[str, Any]): Dictionary of argument names and values. Returns: str: Formatted string of arguments in the form "key1='value1', key2=value2". '=z, )itemsrstrappendjoin) args_dictformatted_argskv v_formatteds r$ format_argsrAkseN!",Q"4"4!Ah#a& 1[M23" 99^ $$r4c,\rSrSrSrSSjrSSjrSrg) Params|zBase class for parameter handling in transforms. Args: format (Any): The format of the data this parameter object will process. label_fields (Sequence[str] | None): List of fields that are joined with the data, such as labels. cXlX lgNformat label_fields)selfrHrIs r$__init__Params.__init__s  (r4c4URURS.$)zReturn a dictionary containing the private parameters of this object. Returns: dict[str, Any]: Dictionary with format and label_fields parameters. rGrGrJs r$to_dict_privateParams.to_dict_privates++t7H7HIIr4rGN)rHr rIzSequence[str] | None)returndict[str, Any])__name__ __module__ __qualname____firstlineno____doc__rKrO__static_attributes__r4r$rCrC|s)Jr4rCc`\rSrSrSrSSSjjr\\SSj55rSSjr SSjr S Sjr S!S jr S"S jr S#S jrS$S jrS!S jrSSjrS%S&Sjjr\S'Sj5r\S(Sj5r\S'Sj5r\S'Sj5rS!SjrS)SjrS*SjrS!SjrSSjrS+SjrSrg), DataProcessora3Abstract base class for data processors. Data processors handle the conversion, validation, and filtering of data during transformations. Args: params (Params): Parameters for data processing. additional_targets (dict[str, str] | None): Dictionary mapping additional target names to their types. NcXlUR/Ul0Ul[ 5UlUbUR U5 ggrF)paramsdefault_data_name data_fieldsis_sequence_inputr label_manager add_targets)rJr^additional_targetss r$rKDataProcessor.__init__sD  22324)^  )   / 0 *r4c[e)zXReturn the default name of the data field. Returns: str: Default data field name. )NotImplementedErrorrNs r$r_DataProcessor.default_data_names "!r4cUR5HBup#X0R:XdMX R;dM'URRU5 MD g)zFAdd targets to transform them the same way as one of existing targets.N)r8r_r`r:)rJrdr>r?s r$rcDataProcessor.add_targetssB&,,.DA***q8H8H/H  ''*/r4cg)zmValidate input data before processing. Args: data (dict[str, Any]): Input data dictionary to validate. NrY)rJr2s r$ensure_data_validDataProcessor.ensure_data_validr4cg)zyValidate transforms before applying them. Args: transforms (Sequence[object]): Sequence of transforms to validate. NrY)rJ transformss r$ensure_transforms_valid%DataProcessor.ensure_transforms_validrnr4c~[U5nURX5nURU5nURU5$)zProcess data after transformation. Args: data (dict[str, Any]): Data dictionary after transformation. Returns: dict[str, Any]: Processed data dictionary. )r3_process_data_fieldsremove_label_fields_from_data_convert_sequence_inputsrJr2rs r$ postprocessDataProcessor.postprocesss>$((511$7,,T22r4c[UR5[UR55-HnURX1UU5X'M U$rF)setr`r1_process_single_fieldrJr2r data_names r$rt"DataProcessor._process_data_fieldssFT--.TYY[1AAI"88OUZ[DOB r4cURX#5nUS:Xa[U5S:XaUR5nURX#SS9$)N keypointsrfrom direction)filterlen_create_empty_keypoints_arraycheck_and_convert)rJr~ field_datars r$r|#DataProcessor._process_single_fieldsG[[3  #J1(<;;=J%%j6%JJr4c[R"/[RS9RS[ UR R 55$)Ndtyper)rarrayfloat32reshaperr^rHrNs r$r+DataProcessor._create_empty_keypoints_arrays3xx"**-55aT[[=O=O9PQQr4c[UR5[UR55-H:nURR US5(dM&XR 5X'M< U$)NF)r{r`r1ragettolistrJr2r~s r$rv&DataProcessor._convert_sequence_inputssZT--.TYY[1AAI%%)))U;;"&/"8"8":B r4c[U5n[UR5[UR55-Han[ X[ 5(a8SUR U'[R"X[RS9X'MRSUR U'Mc URU5n[UR5[UR55-HnURXUSS9X'M g)zfProcess data before transformation. Args: data (dict[str, Any]): Data dictionary to preprocess. TrFtorN) r3r{r`r1rrrarrradd_label_fields_to_datarr}s r$ preprocessDataProcessor.preprocesss$T--.TYY[1AAI$/84448&&y1"$((4?"**"M49&&y1 B,,T2T--.TYY[1AAI"44T_eW[4\DOBr4cURRS:XaURX5 U$US:Xa URO URnU"X5$)aCheck and convert data between Albumentations and external formats. Args: data (np.ndarray): Input data array. shape (ShapeType): Shape information containing dimensions. direction (Literal["to", "from"], optional): Conversion direction. "to" converts to Albumentations format, "from" converts from it. Defaults to "to". Returns: np.ndarray: Converted data array. albumentationsr)r^rHcheckconvert_to_albumentationsconvert_from_albumentations)rJr2rr process_funcs r$rDataProcessor.check_and_convertsN& ;;  !1 1 JJt #K9Bd9Jt55PTPpPp D((r4cg)zFilter data based on shapes. Args: data (np.ndarray): Data to filter. shape (ShapeType): Shape information containing dimensions. Returns: np.ndarray: Filtered data. NrYrws r$rDataProcessor.filterrnr4cg)zValidate data structure against shape requirements. Args: data (np.ndarray): Data to validate. shape (ShapeType): Shape information containing dimensions. NrYrws r$rDataProcessor.check*rnr4cg)zConvert data from external format to Albumentations internal format. Args: data (np.ndarray): Data in external format. shape (ShapeType): Shape information containing dimensions. Returns: np.ndarray: Data in Albumentations format. NrYrws r$r'DataProcessor.convert_to_albumentations4rnr4cg)zConvert data from Albumentations internal format to external format. Args: data (np.ndarray): Data in Albumentations format. shape (ShapeType): Shape information containing dimensions. Returns: np.ndarray: Data in external format. NrYrws r$r)DataProcessor.convert_from_albumentationsErnr4cURR(dU$[UR5[UR 55-H+nXR (dMUR X5X'M- U$)zAdd label fields to data arrays. This method processes label fields and joins them with the corresponding data arrays. Args: data (dict[str, Any]): Input data dictionary. Returns: dict[str, Any]: Data with label fields added. )r^rIr{r`r1size_process_label_fieldsrs r$r&DataProcessor.add_label_fields_to_dataVsc{{''KT--.TYY[1AAI?''"88IDOB  r4cXnURRbfURRHLnURXU5 URR X$X5n[ R "X545nX MN U$rF)r^rI_validate_label_field_lengthrb process_fieldrhstack)rJr2r~ data_array label_fieldencoded_labelss r$r#DataProcessor._process_label_fieldslsx_ ;; # # /#{{77 11$;O!%!3!3!A!A)Z^Zk!lYY 'CD % 8 r4c [X5[X5:wa.[SUSUS[X5S[X5S3 5eg)NzThe lengths of z and z do not match. Got z respectively.)rr0)rJr2r~rs r$r*DataProcessor._validate_label_field_lengthvsa t 3t'8#9 9!)E+?4?+,E#d6G2H1IY  :r4cURR(dU$[UR5[UR 55-H:nXR (dUR U5 M)URX5 M< U$)zRemove label fields from data arrays and restore them as separate entries. Args: data (dict[str, Any]): Input data dictionary with combined label fields. Returns: dict[str, Any]: Data with label fields extracted as separate entries. )r^rIr{r`r1r_handle_empty_data_array_remove_label_fieldsrs r$ru+DataProcessor.remove_label_fields_from_data}sm{{''KT--.TYY[1AAI?''--d3  % %d 6 B  r4cURRb:URRHnURR5X'M! ggrF)r^rIrbhandle_empty_data)rJr2rs r$r&DataProcessor._handle_empty_data_arraysB ;; # # /#{{77 $($6$6$H$H$J! 8 0r4cZURRcgXn[URR5nURSU- n[ URR5H.upgUSS2XV-4nUR R X'U5X'M0 USS2SU24X'g)Nr)r^rIrr enumeraterb restore_field) rJr2r~rnum_label_fieldsnon_label_columnsidxrrs r$r"DataProcessor._remove_label_fieldss ;; # # + _ t{{778&,,Q/2BB )$++*B*B C C'+<+B(BCN $ 2 2 @ @Yg hD !D%Q(:):(:%:;r4)r`rarbr^rF)r^rCrdzdict[str, str] | None)rQr9)rdzdict[str, str]rQNone)r2rRrQr)rpzSequence[object]rQr)r2rRrQrR)r2rRr ShapeTyperQrR)r~r9rr rrrQr )rQ np.ndarray)r)r2rrrrzLiteral['to', 'from']rQr)r2rrrrQr)r2rrrrQr)r2rRr~r9rQr)r2rRr~r9rr9rQr)r2rRr~r9rQr)rSrTrUrVrWrKpropertyrr_rcrlrqrxrtr|rrvrrrrrrrrrrurrrXrYr4r$r[r[s/ 1""+    3 KR ]0,0 )))) )  )6                    ,*K 4> ))r4c[U[5(a[U5[U54$[U5[U54$)atEnsure output is of the same type (int or float) as the input parameter. Args: min_val (Number): Minimum value. max_val (Number): Maximum value. param (Number): Original parameter used to determine the output type. Returns: tuple[int, int] | tuple[float, float]: Tuple with values converted to int if param is int, otherwise values remain as float. )rintfloat)rrrs r$ensure_int_outputrs8",6eS+A+ACL#g, 'ggX]^eXfGggr4c[U[R5(a[R"U5nU$[U[5(a[ [ [U55nU$)zEnsure that numpy arrays are contiguous in memory. Args: arg (np.ndarray | Sequence[np.ndarray]): A numpy array or sequence of numpy arrays. Returns: np.ndarray | list[np.ndarray]: Contiguous array(s) with the same data. )rrrascontiguousarrayrlistmapensure_contiguous_output)args r$rrsR#rzz""""3' J C " "3/56 Jr4cgrFrYrrrs r$to_tuplers r4cgrFrYrs r$rrs r4c L[X5 [U[5(a[U5up4O9[U[5(a[ U[ SU55up4O [S5eUb[X4U5up4[X4[U[[45(aU5$U5$)aConvert input argument to a min-max tuple. This function processes various input types and returns a tuple representing a range. It handles single values, sequences, and can apply optional low bounds or biases. Args: param (tuple[float, float] | float | tuple[int, int] | int): The primary input value. Can be: - A single int or float: Converted to a symmetric range around zero. - A tuple of two ints or two floats: Used directly as min and max values. low (tuple[float, float] | float | None, optional): A lower bound value. Used when param is a single value. If provided, the result will be (low, param) or (param, low), depending on which is smaller. Cannot be used together with 'bias'. Defaults to None. bias (float | int | None, optional): A value to be added to both elements of the resulting tuple. Cannot be used together with 'low'. Defaults to None. Returns: tuple[int, int] | tuple[float, float]: A tuple representing the processed range. - If input is int-based, returns tuple[int, int] - If input is float-based, returns tuple[float, float] Raises: ValueError: If both 'low' and 'bias' are provided. TypeError: If 'param' is neither a scalar nor a sequence of 2 elements. Examples: >>> to_tuple(5) (-5, 5) >>> to_tuple(5.0) (-5.0, 5.0) >>> to_tuple((1, 10)) (1, 10) >>> to_tuple(5, low=3) (3, 5) >>> to_tuple(5, bias=1) (-4, 6) Notes: - When 'param' is a single value and 'low' is not provided, the function creates a symmetric range around zero. - The function preserves the type (int or float) of the input in the output. - If a sequence is provided, it must contain exactly 2 elements. rzEArgument 'param' must be either a scalar or a sequence of 2 elements.) rrrrrrr TypeErrorrrrr)rrrrrs r$rr sb#%""+E2 E4 )%fc1BC_`` %g= W 53PU,8W8Wu ee]d eer4)r#np.ndarray | torch.TensorrQtuple[int, int])r*rrQztuple[int, int, int])r2rRrQr)r<rRrQr9)rz.float | Sequence[int] | Sequence[float] | Noner float | NonerQr)rzSequence[Number]rQtuple[Number, Number])rrrz Number | NonerQr)rrrrrrrQr)rrrrrrrQz%tuple[int, int] | tuple[float, float])rz!np.ndarray | Sequence[np.ndarray]rQznp.ndarray | list[np.ndarray])NN)rzint | tuple[int, int]rzint | tuple[int, int] | NonerrrQr)rzfloat | tuple[float, float]rz"float | tuple[float, float] | NonerrrQztuple[float, float])rz-float | tuple[float, float] | tuple[int, int]rz4float | tuple[float, float] | tuple[int, int] | NonerrrQz%tuple[float, float] | tuple[int, int])*rW __future__rabcrrcollections.abcrnumbersrtypingrr r r r numpyr!albumentations.core.label_managerr serializationrtype_definitionsrrrdictrrr%r+r3rArCr[rrrrrrrrYr4r$rs##$>>:'* 34c9 : ?4@4F2%"J\3J.P